The present invention relates to a method and system for evaluating classification results from classification methods which proceed in computer-assisted fashion and in which objects to be classified are sensed using sensors over a period of time, and are repeatedly classified with the inclusion of specific quality parameters for an object class. A quality parameter of this kind is a parameter number or another qualifying indication within a preselected range, and is expressive of the certainty that the object belongs to the particular object class and not to a different object class.
Observations of objects are evaluated today in many application sectors, e.g., in machine vision in industrial production processes, in safety-related applications, for sensing the surroundings of vehicles, etc. These observations are furnished by measurement devices that can comprise, for example, one or more sensors that in some cases is different, e.g., radar, ultrasound, video in the visible and infrared region, lidar, laser, range imager.
One goal when processing object data obtained in this fashion is classification. “Classification” is understood to be the allocation of objects to specific object classes. A class serves to describe multiple objects that are considered, on the basis of similar features, to belong together. Classification systems for the assignment of objects to object classes are used, for example, in industrial production. Sensing and object classification of the surroundings of a motor vehicle while driving also promises great benefits.
Classification according to conventional classification methods has the disadvantage that, especially in borderline cases with regard to allocation of the sensed object to a class, an elevated risk of misclassification exists. In borderline cases, for example, there is a high probability that in the context of individual classification results, the object will be attributed to the wrong object class. Outliers in the context of classification of an object, resulting, e.g., from measurement errors, also have a negative effect on the stability and robustness of conventional classification methods.
The conventional methods furthermore offer only a yes/no classification of the sensed object. These results are insufficient for situations that require a more differentiated classification result, for example in the context of estimating accident hazards for a motor vehicle. One reason for this is that in the context of a classification repeated over time, an object is allocated first to one class and then, as the object is tracked, later on to a different class; this instability means that a clearly interpretable result is not supplied. Information as to how certain it is that the object might not also belong to one of the other classes would therefore be additionally important.